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Genome-Wide DNA Methylation Patterns in Persistent Attention-Deficit/Hyperactivity Disorderand in Association With Impulsive and Callous TraitsMeijer, Mandy; Klein, Marieke; Hannon, Eilis; van der Meer, Dennis; Hartman, Catharina;Oosterlaan, Jaap; Heslenfeld, Dirk; Hoekstra, Pieter J; Buitelaar, Jan; Mill, JonathanPublished in:Frontiers in Genetics
DOI:10.3389/fgene.2020.00016
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Behavioral and Psychiatric Genetics,a section of the journalFrontiers in Genetics
Received: 08 October 2019Accepted: 07 January 2020Published: 31 January 2020
Citation:Meijer M, Klein M, Hannon E,van der Meer D, Hartman C,Oosterlaan J, Heslenfeld D,
Hoekstra PJ, Buitelaar J, Mill J andFranke B (2020) Genome-Wide DNA
Methylation Patterns in PersistentAttention-Deficit/Hyperactivity
Disorder and in Association WithImpulsive and Callous Traits.
Front. Genet. 11:16.doi: 10.3389/fgene.2020.00016
ORIGINAL RESEARCHpublished: 31 January 2020
doi: 10.3389/fgene.2020.00016
Genome-Wide DNA MethylationPatterns in Persistent Attention-Deficit/Hyperactivity Disorder and inAssociation With Impulsive andCallous TraitsMandy Meijer1, Marieke Klein1,2, Eilis Hannon3, Dennis van der Meer4,5,Catharina Hartman6, Jaap Oosterlaan7,8, Dirk Heslenfeld7, Pieter J. Hoekstra6,Jan Buitelaar9,10,11, Jonathan Mill 3 and Barbara Franke1,12*
1 Department of Human Genetics, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center,Nijmegen, Netherlands, 2 Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht,Netherlands, 3 Medical School, University of Exeter, Exeter, United Kingdom, 4 NORMENT, Division of Mental Health andAddiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway, 5 Faculty of Health,Medicine and Life Sciences, School of Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands,6 Department of Psychiatry, University Medical Center Groningen, University of Groningen, Groningen, Netherlands,7 Experimental and Clinical Neuropsychology Section, Vrije Universiteit Amsterdam, Amsterdam, Netherlands, 8 EmmaNeuroscience Group, Department of Pediatrics, Amsterdam Reproduction & Development, Emma Children's Hospital,Amsterdam UMC, University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, Netherlands, 9 Donders Centre forCognitive Neuroimaging, Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, Netherlands,10 Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, Netherlands, 11 Karakter Child andAdolescent Psychiatric University Centre, Nijmegen, Netherlands, 12 Department of Psychiatry, Donders Institute for Brain,Cognition and Behavior, Radboud University Medical Center, Nijmegen, Netherlands
Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder thatoften persists into adulthood. ADHD and related personality traits, such as impulsivityand callousness, are caused by genetic and environmental factors and their interplay.Epigenetic modifications of DNA, including methylation, are thought to mediate betweensuch factors and behavior and may behave as biomarkers for disorders. Here, we set outto study DNA methylation in persistent ADHD and related traits. We performedepigenome-wide association studies (EWASs) on peripheral whole blood fromparticipants in the NeuroIMAGE study (age range 12–23 years). We comparedparticipants with persistent ADHD (n = 35) with healthy controls (n = 19) andwith participants with remittent ADHD (n = 19). Additionally, we performed EWASs ofimpulsive and callous traits derived from the Conners Parent Rating Scale and the Callous-Unemotional Inventory, respectively, across all participants. For every EWAS, the linearregression model analyzed included covariates for age, sex, smoking scores, andsurrogate variables reflecting blood cell type composition and genetic background. Weobserved no epigenome-wide significant differences in single CpG site methylationbetween participants with persistent ADHD and healthy controls or participants withremittent ADHD. However, epigenome-wide analysis of differentially methylated regionsprovided significant findings showing that hypermethylated regions in the APOB and
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LPAR5 genes were associated with ADHD persistence compared to ADHD remittance(p = 1.68 * 10−24 and p = 9.06 * 10−7, respectively); both genes are involved in cholesterolsignaling. Both findings appeared to be linked to genetic variation in cis. We found neithersignificant epigenome-wide single CpG sites nor regions associated with impulsive andcallous traits; the top-hits from these analyses were annotated to genes involved inneurotransmitter release and the regulation of the biological clock. No link to geneticvariation was observed for these findings, which thus might reflect environmentalinfluences. In conclusion, in this pilot study with a small sample size, we observedseveral DNA-methylation–disorder/trait associations of potential significance for ADHDand the related behavioral traits. Although we do not wish to draw conclusions beforereplication in larger, independent samples, cholesterol signaling and metabolism may beof relevance for the onset and/or persistence of ADHD.
Keywords: persistent attention-deficit/hyperactivity disorder, remittent attention-deficit/hyperactivity disorder,callous traits, impulsivity, DNA methylation, epigenome-wide association study
INTRODUCTION
Attention-deficit/hyperactivity disorder (ADHD) is aneurodevelopmental disorder with a prevalence of 5% inchildren (Polanczyk and Rohde, 2007). Of these children, 65%show persistent symptoms of inattention and/or hyperactivityand impulsivity during adulthood (Faraone et al., 2015), but itremains unknown, why some patients show remittance andothers persistence of the disorder. Based on twin studies, theheritability of the disorder has been estimated to be ~74%(Faraone and Larsson, 2019), and a recent genome-wideassociation study assessed the narrow-sense heritability basedon common genetic variants to be ~22% (Demontis et al., 2019).Several studies have also indicated the importance of theenvironment in the development of ADHD, such as prenatalmaternal stress, prenatal exposure to toxins, low birth weight(Sciberras et al., 2017), and postnatal stress (Roskam et al., 2014).
One of the core symptoms of ADHD is an increased level ofthe behavioral trait impulsivity. Another trait associated withADHD (Fowler et al., 2009), callousness, is predictive of offensiveand aggressive behavior (Watson and Clark, 2019; Dochertyet al., 2019). Both traits are normally distributed in the generalpopulation (Chamorro et al., 2012; Georgiev et al., 2013), and theextreme ends of their spectra are marked as maladaptive andpathological (McLennan, 2016). Both genes (Brikell et al., 2018;Moore et al., 2019) and the environment (Gescher et al., 2018;Waller et al., 2018) play important roles in the etiology ofimpulsive and callous traits.
Environmental adversities may act on behavior throughepigenetic mechanisms (Kubota et al., 2012). Epigeneticmechanisms control gene expression downstream of bothenvironmental and genetic (risk) factors; for example, themethylation of DNA at CpG sites can change in response toan environmental stimulus (Kubota et al., 2012), which can theninfluence the expression levels of genes, and some differentiallymethylated genes linked to impulsive and callous behaviors andrelated disorders like ADHD have recently been described
2
(Waltes et al., 2016; Hamza et al., 2017; Gescher et al., 2018).Since environmental factors can play a role in the onset ofADHD and associated symptoms (Thapar and Cooper, 2016),it is to be expected that DNAmethylation differs between healthycontrols and people with ADHD (symptoms), which mightinfluence the transcription of genes regulating behavior, assummarized by Palumbo and coworkers for aggressivebehavior (Palumbo et al., 2018).
Targeted DNA methylation studies, mostly performed inchildren, reported on genes in the dopaminergic, adrenergic,and serotonergic system associated with ADHD (Hamza et al.,2017; Dall'Aglio et al., 2018). Only few epigenome-wideassociation studies (EWAS) of ADHD and ADHD symptomsin the general population have been performed. Such studieswere performed in children with and without ADHD (age 7–12years) (Wilmot et al., 2016), for symptom trajectories in a generalpopulation cohort (at birth, age 7, and 7–15 years) (Walton et al.,2017), as well as for an adult cohort of individuals from thegeneral population (age 18–38 years) (van Dongen et al., 2019).Given the fact that a percentage of children with ADHD showremission of the disorder (Biederman et al., 2010), it may be ofrelevance to study DNAmethylation patterns related to the onsetof ADHD as well as with its persistence. Up to now, no EWASinvestigated the clinical persistence of ADHD into adulthood,representing a lack of hypothesis-generating studies of potentialepigenetics-related molecular mechanisms of persistence/remittance of ADHD. Similar to the situation for ADHD, onlyfew previous EWASs investigated the association of DNAmethylation and impulsive and aggressive traits, and thoseinvolved general population samples, only (van Dongen et al.,2015; Cecil et al., 2018; Montalvo-Ortiz et al., 2018).
In the current pilot study, we aimed to identify methylationdifferences linked to ADHD persistence, impulsivity, andcallousness in the NeuroIMAGE cohort (von Rhein et al., 2015).We hypothesized to find DNA methylation differences betweenhealthy controls and participants with persistent ADHD, whereasparticipants with remittent ADHD are expected to form an
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intermediate group. To our knowledge, this is the first studyinvestigating the epigenome of participants with persistent andremittent ADHD. The two behavioral traits we studied are knownto be present as continuous traits in the general population and areclosely linked to ADHD, but had not directly been selected for inparticipants of the NeuroIMAGE cohort, enabling us to performquantitative trait analysis in the mixed population of healthy andaffected individuals in the NeuroIMAGE cohort.
METHODS
CohortA subset of 72 independent individuals (persistent ADHD n =35, remittent ADHD n = 18, healthy control n = 19) wereselected based on ADHD symptoms (i.e. sampling groups tocover the extremes of the distribution) from the longitudinalNeuroIMAGE cohort (official cohort n = 751 children withADHD and n = 318 healthy controls) (Cecil et al., 2018).Participants with remittent ADHD were the scarest groupamong the three and those were selected first. We included allremitters with complete data availability and DNA isolationfrom blood. However, inclusion was restricted to one remitterper family. We then matched participants with persistent ADHDand healthy controls to the remitters in terms of age, sex, IQ, andperceived stress using the R package MatchIt v2.4 until reachingthe number of 72, excluding family members. Inclusion criteriawere European Caucasian descent, IQ > 70, and absence of adiagnosis of autism, epilepsy, general learning difficulties, andknown genetic disorders. Participants with a current majordepression were excluded. Patients and controls were recruitedbetween 2003 and 2006 (Supplementary Table 1). In 2008–2009, participants were re-assessed by telephone interviews, withthe same diagnostic criteria where similar loss-of-follow up rateswere observed for control (20%) and ADHD (21%) families.Between 2009 and 2012, participants were re-assessed again.During this third assessment, the blood samples for ourepigenetics study were taken from the participants. Allparticipants gave written informed consent, and the study wasapproved by the regional ethics committee (Centrale CommissieMensgebonden Onderzoek: CMO Regio Arnhem Nijmegen;2008/163; ABR: NL23894.091.08).
Assessment of ADHD, Impulsivity, andCallous TraitsADHD diagnosis was made in accordance with the DSM-IV(Association, 2000) based on the Dutch version of the ParentalAccount of Children's symptoms (PACS) at baseline (Taylor,1991) and based on the Schedule for Affective Disorders andSchizophrenia for children (K-SADS) during the first and secondfollow up, administered to both parents and children to combineinformation for diagnosis from multiple sources (Kaufman et al.,1997). Participants who were diagnosed with ADHD had ≥6hyperactive/impulsive and/or inattentive symptoms, met theDSM-IV criteria for pervasiveness and impairment (measuresderived from the K-SADS), and showed an age of onset before 12
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years. The presence of any other psychiatric disorder wasassessed with a procedure similar to the ADHD interview(combination of K-SADS and CPRS).
The scores derived from the PACS and K-SADS werecombined with the Conners Teacher Rating Scale: Longversion for children <18 years (Conners et al., 1998a) and theConners Adult ADHD Rating Scale-Self Report: Long version forparticipants ≥18 years (Conners et al., 1997), depending on theage of the participant (cut-off 18 years). Participants originallydiagnosed with ADHD, but not meeting the ADHD diagnosticcriteria at follow-up anymore, were classified as remitters(Francx et al., 2015). We did not include participants in ouranalysis that developed late-onset ADHD. All questionnaireswere administered by well-trained research-assistants.Additional information about the diagnostic criteria can befound in the publication of von Rhein and coworker (vonRhein et al., 2015).
To measure impulsivity, the raw scores of the Global restless–impulsive scale I of the Conners Parent Rating Scale (CPRS) wereused (Conners et al., 1998b). In total, seven items were scoredwith either 0 or 1 point: restless or overactive; irritable orimpulsive; does not finish the things he/she starts with; hassloppy handwriting; cannot sit still or is wobbling nervously;disturbs other children; and wants desires to be met immediately.Total scores of the Global restless–impulsive scale I of the CPRScan be found in Table 1 and Supplementary Figure 1.
To measure callous traits, participants completed theInventory of Callous-Unemotional Traits (Kimonis et al.,2008). We created a combined score for callousness byselecting the following nine items reflecting callous behavior:What I think is right and wrong is different from what otherpeople think; I do not care whom I hurt to get what I want; I feelbad or guilty when I do something wrong*; I am concerned aboutthe feelings of others*; I apologize to persons I hurt*; I try not tohurt others' feelings*; I do not feel remorseful when I dosomething wrong; The feelings of others are unimportant tome; and I do things to make others feel good*. Questions with an* indicate inverse questions, meaning that scores were inverted tocalculate callous scores. Questions were scored on a scale of 0–3(0 = not at all true, 1 = somewhat true, 2 = very true, 3 =definitely true). The final score for a subject could range between0 and 27. The combined score we created reflects a continuouscallous trait, which showed a wider distribution in the selectedpopulation than the unemotional component of the behavior, asit also includes offensive behavior.
DNA Methylation ProfilingDNA was isolated from peripheral blood at the department ofHuman Genetics of the Radboud University Medical Center inNijmegen using standard protocols, and an epigenome-wideanalysis was performed with the Infinium® MethylationEPICBeadChip (Illumina, San Diego, USA) by Life & Brain GmbH,Bonn, Germany. The readEPIC() function of the wateRmelonpackage was used to import methylation data and to compareintensities of methylated (M) and unmethylated (U) DNA(Pidsley et al., 2013). Bisulphite conversion rates were
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calculated, with samples showing rates < 0.8 excluded.Correlation between 59 common SNPs was used to identifyduplicate samples. No probes were excluded based on bisulfiteconversion rates or sample duplicates. All probes with bead count<3 in >5% of the samples (n = 9,735) or >1% of samples with adetection p-value >0.05 (n = 4,126) were removed. Beta-values(methylated/unmethylated) were calculated for all probes and werematched to the probe annotation of the EPIC array v1.0 B4(Illumina Inc., 2017, San Diego, USA) to be used for subsequentanalysis. Probes known to cross-hybridize with each other wereremoved, as were CpG sites with a minor allele frequency <0.05, tobe sure effects are because of differentially methylation rather thangenomic variation at a CpG site (McCartney et al., 2016). Probes onthe X-chromosome that passed quality control were used to verifysex in all participants, by multidimensional scaling (MDS, ncomponents = 2). With the DNA Methylation Age Calculator(https://dnamage.genetics.ucla.edu/home) the age of the subjects atthe time of sampling was calculated (Horvath, 2013). Of the866,554 probes on the array, 853,539 CpG sites passed all QC steps.
Assessment of Smoking as CovariateSince smoking is known to affect DNA methylation (Zeilingeret al., 2013), and participants with ADHD indicated to besmokers more often than healthy controls, we corrected forsmoking in our model. A smoking score was calculated perindividual, based on 177 CpG sites currently known to bedifferentially methylated by smoking (Zeilinger et al., 2013; Liet al., 2018). The average methylation effect of these CpG siteswas calculated based on a discovery and replication cohort aspreviously described by Elliot and co-workers (Elliott et al.,2014). These smoking scores were used as covariate in thestatistical model to account for an individual's smoking statusinstead of the self-reported smoking status, since this scoreincorporates past smoking, which was not available atsufficient depth from questionnaires.
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Surrogate variable (SV) analysis is a method to captureheterogeneity across the data not caused by the variables ofinterest and is sensitive enough to detect possible differences ofblood cell composition (SV 1 showed a Pearson's correlation =0.9 with the most abundant blood cell type), ethnical descent,and chip position (Leek and Storey, 2007; Kaushal et al., 2017).SVs were created via the “sva” package in R (Leek et al., 2012),and all 10 SVs from the model were included as covariates in thestatistical model.
Statistical AnalysisAll analyses were performed in R 3.4.4. Differences indemographics were tested with a one-way-ANOVA. Methylationdifferences at the single CpG site level were analyzed by means of alinear model with sex, age, smoking score, and 10 SVs as covariates,since sex, age, and smoking are known to affect DNA methylation(Zeilinger et al., 2013; van Dongen et al., 2016). We did not includestimulant medication as a covariate, since there is no evidence thatit affects DNA methylation (Wilmot et al., 2016). To preventovercorrection of the model by including additional covariates ofunknown relevance, we included the 10 SVs. We ran the followingcomparisons: (1) healthy controls (n = 19) and participants withpersistent ADHD (n = 35), (2) participants with remittent ADHD(n = 18) and with persistent ADHD. Two additional EWASs wereperformed for impulsive and callous traits as continuous measures.The study-wide epigenome-wide significant p-value threshold wasset at p < 9 * 10−8 (Mansell et al., 2019). However, given theexploratory character of our study, we also considered nominallysignificant results with p < 5 * 10−5, unadjusted for the number ofEWASs performed. Inflation factors for the regression model werecalculated as the square root of the expected median of a chi-squared statistics distribution divided by the median of the chi-statistics with one degree of freedom.
The “Comb-p” Python module was used to identifydifferentially methylated regions (DMR) in all analyses(Pedersen et al., 2012). A region was considered to be a DMR
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TABLE 1 | Characteristics of samples included in the NeuroIMAGE DNA methylation study.
Healthy control Remittent ADHD Persistent ADHD Total/average
P-value(one-way ANOVA)
N 19 18 35 71% Male 68 67 77 73 0.6683AgeMean (SD)
20.12 (2.83) 21.69 (3.38) 20.93 (2.64) 20.91(2.90)
0.2622
IQMean (SD)
113.83 (10.35) 101.17 (16.11) 96.06 (17.56) 101.94 (16.95) 0.0003*control differs from remittent (4.22) and persistent
(6.06)DNA methylation smokingscore
5.39 (2.01) 5.82 (2.79) 6.54 (2.64) 6.05 (2.56) 0.2635
Impulsivity 1.294 (1.49) 7.611 (5.94) 9.914 (5.43) 7.229 (6.02) <0.0001*control differs from remittent (5.34) and persistent
(8.34)Callous 7.00 (2.22) 9.389 (4.70) 11.086 (5.31) 9.6 (4.84) 0.0114*
control differs from remittent (2.22) and persistent(4.36)
*Significant p-value (<0.05).In the p-value (obtained with one-way-ANOVA) column, number between brackets reflect the Tukey's multiple comparison test q-value for the mentioned groups.ADHD, attention-deficit/hyperactivity disorder; ANOVA, analysis of variance.
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if at least two methylation sites were differentially methylatedwithin a 500 bp window (Šidák-corrected p-value < 0.05)(Crawford et al., 2018).
Biological Interpretation of Top-FindingsTo assess whether the observed levels of CpG methylationdepend on genetic variation, methylation quantitative trait loci(meQTL) were assessed for all nominally significant CpG sitesfrom the former analyses. Databases on meQTLs in peripheralblood during adolescence (Gaunt et al., 2016) and in the dorsallateral prefrontal cortex in adults (Ng et al., 2017) were used. ASNP with <1 Mb distance to the corresponding CpG site wasconsidered to be a trans-meQTL. In addition, expressionquantitative trait methylations (eQTM) were assessed via thegenome browser of iMETHYL, based on experiments inneutrophils isolated from 100 healthy Japanese subjects(Hachiya et al., 2017).
RESULTS
The characteristics of the study sample are displayed in Table 1.There were no significant differences between healthy controls(n = 19) and participants with remittent (n = 18) and withpersistent ADHD (n = 35) for age, sex, and smoking score (Table1), for which the samples had been matched. Healthy controlsshowed significantly higher IQ than participants with remittentand persistent ADHD (p = 0.0003, Tukey's multiple comparisontest q = 4.22 and 6.06, respectively). Impulsivity (p < 0.0001,Tukey's multiple comparison test q = 5.34 and 8.34) andcallousness (p = 0.0114, Tukey's multiple comparison test q =2.22 and 4.36) scores also differed between participants withADHD and controls. Distributions of impulsive and calloustraits overlapped among the different groups, showing a widespread (Supplementary Figure 1). The continuous traits did notcorrelate with each other (Pearson's r = 0.215, p = 0.073),indicating that the analysis on impulsivity and callousness weresupplementary to each other.
Robust Epigenetic Signatures of Age andTobacco Use Validate DNA MethylationDataTo verify the robustness of our DNA methylation signal, weaimed to validate reported age by measuring DNA methylationage. The average chronological age predicted from methylationdata was 14.78 years (SD = 3.43), and this was strongly correlatedwith actual age (r = 0.650, p = 6.5 * 10−10) (SupplementaryFigure 2A). Individuals with the highest deviation from reportedage were participants with ADHD and smokers. Testing markersof smoking exposure during adulthood, we identified asignificant association between the methylation-derivedsmoking scores and self-reported smoking behavior, withcurrent smokers having higher smoking scores compared tonon-smokers (p = 2.38 * 10−8, Supplementary Figure 2B).
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DNA Methylation Profiles of HealthyControls and Participants With Persistentand Remittent ADHD DifferTo assess DNA methylation profiles associated with persistentADHD, we compared methylation levels of participants withpersistent ADHD with those of healthy controls, estimated byaveraging across all probes on the array included in our analyses.The inflation factor of 0.996 showed that no inflation was presentin the model. Here, no global differences in DNA methylationwere found between participants with persistent ADHD (meanproportion of methylated CpG sites = 0.616, SD = 0.0009) andhealthy controls (mean proportion of methylated CpG sites =0.615, SD = 0.001, p = 0.2246). While no individual probespassed the experiment-wide significance threshold, seven CpGsites showed differential methylation at our more lenient,suggestive significance threshold of p < 5 * 10−5 (Figure 1A,Supplementary Table 2, Supplementary Figure 3A). In thecomparison between persistent and remittent ADHD, globaldifferences in DNA methylation did reach significance (meanproportion of methylated CpG sites persisters = 0.616, SDpersisters = 0.0009; mean proportion of methylated CpG sitesremitters = 0.615, SD remitters = 0.0040; t-test p = 0.00115).Comparing individual DNA methylation differences betweenparticipants with persistent and remittent ADHD, we did notidentify any differentially methylated probes passing theexperiment-wide significance threshold. The inflation factor of0.969 showed that no inflation-depth explanatory analysis onnominally significn was present in the model. Five CpG sitesshowed differential methylation at our more lenient, suggestivesignificance threshold (p < 5 * 10−5) (Figure 1B, SupplementaryTable 2, Supplementary Figure 3B). In-depth explanatoryanalysis on nominally significant sites can be found in theSupplementary Text and corresponding tables.
In the regional analysis, we identified a small DMR in thePPT2 gene (65 bp containing four probes) to be significantlyhypomethylated in participants with persistent ADHDcompared to healthy controls (Šidák-corrected p-value = 0.030,Figure 1C). Two additional significant DMRs were found to beassociated with ADHD persistence in the comparison betweenparticipants with persistent and remittent ADHD aftercorrect ion for mult ip le tes t ing . Both DMRs werehypermethylated in the participants with persistent ADHD(Figures 1D, E); one was located within 200 bp of thetranscription start site of the APOB gene, spanning 485 bp,consisting of 10 measured CpGs (Šidák-corrected p-value = 1.66* 10−24, Figure 1D), and one was present in the LPAR5 gene,spanning 123 bp and containing four probes (Šidák-corrected p-value = 9.06 * 10−7, Figure 1E). Probes in the APOB DMR hadalso been found nominally associated in the single-probe analysis(Supplementary Table 2).
Differential Methylation Observations inPersistent ADHD Might Be GenomicEffectsTo assess whether the differential methylation observed inpersistent ADHD might be dependent on genetic variation,
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meQTLs were assessed. There were 3,460 SNP-meQTL pairs forthe APOB DMR. Forty-seven SNPs were meQTLs for the thirdCpG site in the LPAR5 DMR (Supplementary Table 3). Thissuggests that the differences we observed between participantswith persistent and remittent ADHD might be genomic effects.Moreover, 8.1% of the measured meQTL SNPs—more than onewould expect by chance (X = 5.008, p = 0.025)—were associated(unadjusted p-value < 0.05) with adult, persistent ADHD in arecent GWAS meta-analysis (Rovira et al., 2019), but none of thefindings survived correction for multiple testing.
DNA Methylation Profiles Associated WithImpulsive and Callous TraitsNo epigenome-wide significant CpG sites correlated withimpulsive traits (analyzed in n = 70) (Figure 2A, SupplementaryTable 4) or callousness (Figure 2B, Supplementary Table 4). Forboth analyses, no inflation was present showed by an inflation
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factor of 0.998 and 0.980, respectively. Regional analyses also didnot reveal DMRs associated with either impulsive or callous traits.
DISCUSSION
In the current study, we aimed to 1) identify differences inperipheral blood DNA methylation profiles linked to thepersistence of ADHD, and 2) find DNA methylation levelscorrelated with impulsive and callous traits. To our knowledge,this is the first study in which differences in DNA methylationlevels in participants with persistent and remittent ADHD wereinvestigated. This study showed global DNA methylationdifferences between participants with persistent ADHDcompared to participants with remittent ADHD. Two DMRs,in APOB and LPAR5, were found significantly differentiallymethylated between participants with remittent and persistent
FIGURE 1 | Differential DNA methylation in persistent attention-deficit/hyperactivity disorder (ADHD). −log10(p-values) of the methylation sites are plotted along theirchromosomal position for (A) n = 35 participants with persistent ADHD and n = 19 healthy controls and (B) n = 35 participants with persistent and n = 18participants with remittent ADHD. The horizontal blue line indicates the nominally significance threshold of −log10(5). One differentially methylated region was found in(C) PPT2 for participants with persistent ADHD (red) compared to healthy controls (light blue). Differentially methylated regions were found in (D) APOB and (E)LPAR5 for participants with persistent compared to remittent ADHD (dark blue). Each circle represents a CpG site, of which DNA methylation levels are plotted forthe corresponding chromosomal location. Individual p-values per CpG sites are located next to the site, and a combined p-value is given in the upper right corner.
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ADHD. Moreover, several CpG sites were differentiallymethylated between participants with persistent ADHD andhealthy controls, and participants with persistent and remittentADHD based on a more lenient, suggestive significance level ofp < 5 * 10−5. Lastly, we showed that impulsive and callous traitswere associated with 18 differentially methylated CpG sites atthis lenient threshold.
Global DNA Methylation DifferencesBetween GroupsWe found that global methylation differences were lower inparticipants with persistent ADHD than in healthy controls. It isknown that DNA methylation gradually decreases during aging(Horvath and Raj, 2018), and it has been shown that DNAmethylation based on aging can be accelerated in patients withpsychiatric disorders (Han et al., 2018; Okazaki et al., 2019) and insmokers (Zeilinger et al., 2013). This is consistent with our findingthat individuals with the largest deviation between actual andpredicted age based on DNA methylation (i.e. higher predictedage) were the individuals with ADHD reporting to be smokers.
Blood DNA Methylation Associated WithADHD Persistence Correlated With BrainDNA MethylationWe expected to find biomarkers of ADHD persistence andassociated traits in the DNA isolated from peripheral blood. Suchbiomarkers do not necessarily reflect brain DNA methylationstatus, and we have no information on any causal relationshipswith disease. Some of our findings did show a relatively highcorrelation with methylation levels in different brain regions. Thelack of blood–brain correlation in methylation observed for manyof our findings is not necessarily due to the limited sample size ofour study. A recent methylome-wide association study forschizophrenia (N = 1448) found that most of their top-findingswere also specific for blood, but that half of their sites were locatedin genes that were enriched in brain-specific GO-terms (Chan et al.,2019). These findings suggest that the DNA methylation markerswe identified in blood could also be relevant for brain-relatedprocesses and behaviors.
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Comparison With DNA Methylation Sites inPrevious (Epi)Genetic StudiesPrevious studies investigating the epigenome in ADHD in acandidate gene-based manner have reported differentialmethylation in the serotonin and dopamine system (Hamzaet al., 2017), which had earlier been implicated in ADHDthrough candidate gene-based genetic studies (Faraone andLarsson, 2019). We did not find any genes related to thedopamine and serotonin systems among our most stronglyassociated DNA methylation sites for persistent ADHD. Whilethis does not invalidate either the findings from the epigeneticcandidate-gene studies nor ours, it may suggest that epigeneticeffects in these systems may not be those with the strongest effectsizes. Our observed differential DNA methylation sites also didnot overlap with any of the few published EWASs for ADHD,ADHD symptoms, or aggression (Wilmot et al., 2016; Waltonet al., 2017; van Dongen et al., 2019). The lack of reproducibilitycould be explained by our low sample size, the samplepopulations used (clinical, general population, or mixedcohorts), and the different arrays used for DNA methylationquantification (450K or 850K array).
Based on genetic, rather than epigenetic studies, we did findsupport for the observed differential methylation sites associatedwith ADHD. Our most significant finding hints toward fatty acidmetabolism in persistent ADHD, and genes involved in fatty acidmetabolism and fatty acid oxidation pathways have also beenindicated to be differentially methylated in participants withADHD compared to healthy controls (Wilmot et al., 2016;Walton et al., 2017).
DNA Methylation in Fatty Acid MetabolismPathways Might Be a Marker forPersistent ADHDWe identified both DMRs and single DNA methylation sites inAPOB and LPAR5 to be differentially methylated betweenpart ic ipants with persistent and remittent ADHD.Hypermethylation of APOB in participants with persistentADHD compared to those with remittent ADHD implieslower APOB expression in those with persistent ADHD, butwas not validated. APOB is the primary lipoprotein found on the
FIGURE 2 | Differential DNA methylation in impulsive and callous traits. −log10(p-values) of the methylation sites for (A) impulsive traits measured by the CPRS (n =70) and (B) callousness (n = 71). The horizontal blue line indicates our more lenient suggestive significance threshold of −log10(5).
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surface of low density lipoproteins (LDL), which transports fattyacids and cholesterol throughout the body (Dashti et al., 2011).As indicated above, genes involved in fatty acid metabolism andfatty acid oxidation pathways have been indicated to bedifferentially methylated in patients with ADHD compared tohealthy controls (Wilmot et al., 2016; Walton et al., 2017). This isalso in concordance with previously published literature statingthat lipid levels, specifically LDL levels, in blood are altered inpatients with ADHD (Avcil, 2018; Pinho et al., 2018), and thatLDL and APOB are the most discriminating lipid markers inchildren with ADHD (Irmisch et al., 2011). Moreover, theADHD medication methylphenidate is known to alter bloodlipid profiles (Charach et al., 2009), and a recent GWAS ofADHD found significant genetic correlations between ADHDand HDL cholesterol and LDL cholesterol (Demontis et al.,2019). A potential role of APOB in ADHD is underlined evenmore by the fact that mice knocked-out for the LDL-receptor(LDLR−/−) show hyperactivity (Elder et al., 2008). These micelack the receptor for the LDL particles, containing APOB.However, it should be noted that lipoproteins functioning inthe brain are exclusively produced in the brain, and therefore,blood and brain lipid levels might be very different from eachother within individuals (Wang and Eckel, 2014). It wastherefore important to find that blood DNA methylation levelsfor APOB correlated with brain DNA methylation levels in ouranalysis, suggesting similar expression regulation. APOB can bemodified with oxidized aldehyde products, resulting in oxidizedLDL (oxLDL) (Itabe et al., 2011), which can cause inflammation(Lara-Guzman et al., 2018). Another molecule that is producedby the oxidization process of LDL is LPA (Nsaibia et al., 2017).This molecule promotes inflammation after binding the LPAR5(Ramesh et al., 2018). Mice depleted with LPA5 show nocturnalhyperactivity (Callaerts-Vegh et al., 2012), a second animalmodel in the APOB-related pathway showing ADHDsymptoms. A more elaborate model of the potential role ofAPOB in persistent ADHD can be found in SupplementaryFigure 4 and the Supplementary Text.
Strengths and LimitationsOur study shows several strengths and limitations. To ourknowledge, we are the first to show differential methylationpatterns in a cohort, in which we could compare participantswith persistent and remittent ADHD. Although the sample sizewas small, patients and controls were carefully matched, andtherefore, in terms of demographics, our group was ashomogeneous as possible. Given the small sample size, it ispromising that we find significant DMRs in two genes. However,all findings should be interpreted with caution, and resultsshould be replicated in independent, bigger cohorts.
ConclusionWe showed that hypermethylation of APOB and LPAR5 inperipheral blood might be associated with persistence of ADHD,which is probably due to genetic variation influencing DNAmethylation. These data, which suggest an involvement of fattyacid metabolism, provide new hypotheses for research into themolecular mechanism of persistent ADHD and associated traits.
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DATA AVAILABILITY STATEMENT
The datasets for this article are not publicly available because oflimitations in ethical approvals. A request procedure is in place,based on submission of a short proposal. Requests to access thedatasets should be directed to BF, [email protected] JB, [email protected].
ETHICS STATEMENT
The studies involving human participants were reviewed andapproved by Centrale Commissie Mensgebonden Onderzoek:CMO Reg io Arnhem Ni jmegen ; 2008 /163 ; ABR :NL23894.091.08. Written informed consent to participate in thisstudy was provided by the participants' legal guardian/next of kin.
AUTHOR CONTRIBUTIONS
Study conception and supervision: MM, MK, JM, BF. Obtainedfunding: CH, JO, DH, PH, JB, BF. Provided samples and/or data:CH, JO, DH, PH, JB. Conducted analyses and datainterpretation: MM, MK, EH, DM, BF. Writing group: MM,MK, BF. All authors contributed to manuscript revision, readand approved the submitted version.
FUNDING
This research was supported by an internal grant from theDonders Centre for Medical Neuroscience of Radboudumc.Support was also received from the Dutch National ScienceAgenda for the NWA NeurolabNL project (grant 400 17 602),and from the European Community's Horizon 2020 Programme(H2020/2014–2020) under grant agreement n° 728018(Eat2beNICE). BF was also supported by a personal grant fromthe Netherlands Organization for Scientific Research (NWO)Vici Innovation Program (grant 016-130-669). TheNeuroIMAGE project was supported by NIH GrantR01MH62873, NWO Large Investment Grant 1750102007010,and grants from Radboud University Medical Center, UniversityMedical Center Groningen and Accare, and VU UniversityAmsterdam. This work was also supported by grants fromNWO Brain & Cognition (433-09-242 and 056-13-015) andfrom ZonMW (60-60600-97-193).
ACKNOWLEDGMENTS
The authors would like to thank all participants of theNeuroIMAGE study.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found onlinea t : h t t p s : / /www . f r on t i e r s i n . o r g / a r t i c l e s / 1 0 . 3389 /fgene.2020.00016/full#supplementary-material
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Conflict of Interest: PH has received a research grant from and served on theadvisory board for Shire. JB has served as a consultant, advisory board member,and/or speaker for Eli Lilly, JanssenCilag, Medice, Roche, Shire, and Servier. BFreceived educational speaking fees from Medice.
The remaining authors declare that the research was conducted in the absence ofany commercial or financial relationships that could be construed as a potentialconflict of interest.
Copyright © 2020 Meijer, Klein, Hannon, van der Meer, Hartman, Oosterlaan,Heslenfeld, Hoekstra, Buitelaar, Mill and Franke. This is an open-access article dis-tributed under the terms of the Creative Commons Attribution License (CC BY). Theuse, distribution or reproduction in other forums is permitted, provided the originalauthor(s) and the copyright owner(s) are credited and that the original publication inthis journal is cited, in accordancewith acceptedacademic practice.Nouse, distributionor reproduction is permitted which does not comply with these terms.
January 2020 | Volume 11 | Article 16
